Showing papers by "Indian Institute of Technology Indore published in 2015"

Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions

Abstract: We propose new features for classification of epileptic seizure EEG signals.Features were extracted from PSR of IMFs of EEG signals.We define ellipse area of 2D PSR and IQR of Euclidian distance of 3D PSR as features.LS-SVM classifier has been used for classification with the proposed features.Results were compared with other existing methods studied on the same EEG dataset. Epileptic seizure is the most common disorder of human brain, which is generally detected from electroencephalogram (EEG) signals. In this paper, we have proposed the new features based on the phase space representation (PSR) for classification of epileptic seizure and seizure-free EEG signals. The EEG signals are firstly decomposed using empirical mode decomposition (EMD) and phase space has been reconstructed for obtained intrinsic mode functions (IMFs). For the purpose of classification of epileptic seizure and seizure-free EEG signals, two-dimensional (2D) and three-dimensional (3D) PSRs have been used. New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals. Two measures have been defined namely, 95% confidence ellipse area for 2D PSR and interquartile range (IQR) of the Euclidian distances for 3D PSR of IMFs of EEG signals. These measured parameters show significant difference between epileptic seizure and seizure-free EEG signals. The combination of these measured parameters for different IMFs has been utilized to form the feature set for classification of epileptic seizure EEG signals. Least squares support vector machine (LS-SVM) has been employed for classification of epileptic seizure and seizure-free EEG signals, and its classification performance has been evaluated using different kernels namely, radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernels. Simulation results with various performance parameters of classifier, have been included to show the effectiveness of the proposed method for classification of epileptic seizure and seizure-free EEG signals.

K∗(892)0 and φ(1020) production in Pb-Pb collisions at sNN =2.76 TeV

Abstract: The yields of the K*(892)(0) and phi(1020) resonances are measured in Pb-Pb collisions at root s(NN) = 2.76 TeV through their hadronic decays using the ALICE detector. The measurements are performed in multiple centrality intervals at mid-rapidity (vertical bar y vertical bar <0.5) in the transverse-momentum ranges 0.3

Centrality dependence of particle production in p-Pb collisions at root s(NN)=5.02 TeV

Abstract: We report measurements of the primary charged-particle pseudorapidity density and transverse momentum distributions in p-Pb collisions at root s(NN) = 5.02 TeV and investigate their correlation with experimental observables sensitive to the centrality of the collision. Centrality classes are defined by using different event-activity estimators, i.e., charged-particle multiplicities measured in three different pseudorapidity regions as well as the energy measured at beam rapidity (zero degree). The procedures to determine the centrality, quantified by the number of participants (N-part) or the number of nucleon-nucleon binary collisions (N-coll) are described. We show that, in contrast to Pb-Pb collisions, in p-Pb collisions large multiplicity fluctuations together with the small range of participants available generate a dynamical bias in centrality classes based on particle multiplicity. We propose to use the zero-degree energy, which we expect not to introduce a dynamical bias, as an alternative event-centrality estimator. Based on zero-degree energy-centrality classes, the N-part dependence of particle production is studied. Under the assumption that the multiplicity measured in the Pb-going rapidity region scales with the number of Pb participants, an approximate independence of the multiplicity per participating nucleon measured at mid-rapidity of the number of participating nucleons is observed. Furthermore, at high-pT the p-Pb spectra are found to be consistent with the pp spectra scaled by N-coll for all centrality classes. Our results represent valuable input for the study of the event-activity dependence of hard probes in p-Pb collisions and, hence, help to establish baselines for the interpretation of the Pb-Pb data.

Breast cancer diagnosis using Genetically Optimized Neural Network model

Abstract: One in every eight women is susceptible to breast cancer, at some point of time in her life Early detection and effective treatment is the only rescue to reduce breast cancer mortality Accurate classification of a breast cancer tumor is an important task in medical diagnosis Machine learning techniques are gaining importance in medical diagnosis because of their classification capability In this paper, we propose a new, Genetically Optimized Neural Network (GONN) algorithm, for solving classification problems We evolve a neural network genetically to optimize its architecture (structure and weight) for classification We introduce new crossover and mutation operators which differ from standard crossover and mutation operators to reduce the destructive nature of these operators We use the GONN algorithm to classify breast cancer tumors as benign or malignant To demonstrate our results, we had taken the WBCD database from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, ROC curves and AUC under ROC curves of GONN with classical model and classical back propagation model Our algorithm gives classification accuracy of 9824%, 9963% and 100% for 50–50, 60–40, 70–30 training–testing partition respectively and 100% for 10 fold cross validation The results show that our approach works well with the breast cancer database and can be a good alternative to the well-known machine learning methods

An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

Abstract: The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student’s t-test, the Wilcoxon test, the receiver operating characteristic (ROC) and entropy. These ranked features are fed to various classifiers, namely k-nearest neighbour (KNN), probabilistic neural network (PNN), fuzzy classifier and least squares support vector machine (LS-SVM), for automated classification of focal and non-focal EEG signals using the minimum number of features. The identification of the focal EEG signals can be helpful to locate the epileptogenic focus.

Rapidity and transverse-momentum dependence of the inclusive J/psi nuclear modification factor in p-Pb collisions at root s(NN)=5.02 TeV

Abstract: We have studied the transverse-momentum (pT) dependence of the inclusive J/ψ production in p-Pb collisions at √sNN = 5.02 TeV, in three center-of-mass rapidity (ycms) regions, down to zero pT. Results in the forward and backward rapidity ranges (2.03 < ycms < 3.53 and −4.46 < ycms < −2.96) are obtained by studying the J/ψ decay to µ +µ −, while the mid-rapidity region (−1.37 < ycms < 0.43) is investigated by measuring the e+e − decay channel. The pT dependence of the J/ψ production cross section and nuclear modification factor are presented for each of the rapidity intervals, as well as the J/ψ mean pT values. Forward and mid-rapidity results show a suppression of the J/ψ yield, with respect to pp collisions, which decreases with increasing pT. At backward rapidity no significant J/ψ suppression is observed. Theoretical models including a combination of cold nuclear matter effects such as shadowing and partonic energy loss, are in fair agreement with the data, except at forward rapidity and low transverse momentum. The implications of the p-Pb results for the evaluation of cold nuclear matter effects on J/ψ production in Pb-Pb collisions are also discussed.

Measurement of pion, kaon and proton production in proton–proton collisions at √s = 7 TeV

Abstract: The measurement of primary [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] production at mid-rapidity ([Formula: see text] 0.5) in proton-proton collisions at [Formula: see text][Formula: see text] 7 TeV performed with a large ion collider experiment at the large hadron collider (LHC) is reported. Particle identification is performed using the specific ionisation energy-loss and time-of-flight information, the ring-imaging Cherenkov technique and the kink-topology identification of weak decays of charged kaons. Transverse momentum spectra are measured from 0.1 up to 3 GeV/[Formula: see text] for pions, from 0.2 up to 6 GeV/[Formula: see text] for kaons and from 0.3 up to 6 GeV/[Formula: see text] for protons. The measured spectra and particle ratios are compared with quantum chromodynamics-inspired models, tuned to reproduce also the earlier measurements performed at the LHC. Furthermore, the integrated particle yields and ratios as well as the average transverse momenta are compared with results at lower collision energies.

Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

Abstract: Coronary artery disease (CAD) is the narrowing of coronary arteries leading to inadequate supply of nutrients and oxygen to the heart muscles. Over time, the condition can weaken the heart muscles and may lead to heart failure, arrhythmias and even sudden cardiac death. Hence, the early diagnosis of CAD can save life and prevent the risk of stroke. Electrocardiogram (ECG) depicts the state of the heart and can be used to detect the CAD. Small changes in the ECG signal indicate a particular disease. It is very difficult to decipher these minute changes in the ECG signal, as it is prone to artifacts and noise. Hence, we detect the R peaks from the ECG and use heart rate signals for our analysis. The manual inspection of the heart rate signals is time consuming, taxing and prone to errors due to fatigue. Hence, a decision support system independent of human intervention can yield accurate repeatable results. In this paper, we present a new method for diagnosis of CAD using tunable-Q wavelet transform (TQWT) based features extracted from heart rate signals. The heart rate signals are decomposed into various sub-bands using TQWT for better diagnostic feature extraction. The nonlinear feature called centered correntropy ( CC ) is computed on decomposed detail sub-band. Then the principal component analysis (PCA) is performed on these CC to transform the number of features. These clinically significant features are subjected to least squares support vector machine (LS-SVM) with different kernel functions for automated diagnosis. The experimental results demonstrate better classification accuracy, sensitivity, specificity and Matthews correlation coefficient using Morlet wavelet kernel function with optimized kernel and regularization parameters. Also, we have developed a novel CAD Risk index using significant features to discriminate the two classes using a single number. Our proposed methodology is more suitable in classification of normal and CAD heart rate signals and can aid the clinicians while screening the CAD patients.

Production of Σ(1385)± and Ξ(1530)0 in proton–proton collisions at s√= 7 TeV

Abstract: The production of the strange and double-strange baryon resonances (Sigma (1385)(+/-), Xi (1530)(0)) has been measured at mid-rapidity (vertical bar y vertical bar < 0.5) in proton-proton collisions at root s = 7 TeV with the ALICE detector at the LHC. Transverse momentum spectra for inelastic collisions are compared to QCD-inspired models, which in general underpredict the data. A search for the phi (1860) pentaquark, decaying in the Xi pi channel, has been carried out but no evidence is seen.

Elliptic flow of identified hadrons in Pb-Pb collisions at root(NN)-N-s=2.76 Tev

Abstract: The elliptic flow coefficient (v(2)) of identified particles in Pb-Pb collisions at root s(NN) = 2.76 TeV was measured with the ALICE detector at the Large Hadron Collider (LHC). The results were obtained with the Scalar Product method, a two-particle correlation technique, using a pseudo-rapidity gap of |Delta eta| > 0.9 between the identified hadron under study and the reference particles. The v (2) is reported for pi(+/-), K-+/-, K-S(0), p+(p) over bar, phi, Lambda+(Lambda) over bar, Xi+(Xi) over bar (+) and Omega(-)+(Omega) over bar (+) in several collision centralities. In the low transverse momentum (p(T)) region, p(T) 3 GeV/c.

Zeolitic Imidazole Framework (ZIF) Nanospheres for Easy Encapsulation and Controlled Release of an Anticancer Drug Doxorubicin under Different External Stimuli: A Way toward Smart Drug Delivery System.

Abstract: The conventional drug delivery systems made from organic- or inorganic-based materials suffer from some problems associated with uncontrolled drug release, biocompatibility, cytotoxicity, and so forth. To overcome these problems, zeolitic imidazole framework (ZIF) hybrid materials can be one of the solutions. Here, we report a very easy and successful encapsulation of an anticancer drug doxorubicin inside two ZIFs, namely, ZIF-7 and ZIF-8, which are little explored as drug delivery systems, and we studied the controlled release of the drug from these two ZIFs under external stimuli such as change in pH and upon contact with biomimetic systems. Experimental results demonstrate that ZIF-7 remains intact when the pH changes from physiological condition to acidic condition, whereas ZIF-8 successfully releases drug under acidic condition. Interestingly, both the ZIFs are excellent for drug release when they come in contact with micelles or liposomes. In the case of ZIF-8, the drug delivery can be controlled fo...

Estimation of Single-Phase and Three-Phase Power-Quality Indices Using Empirical Wavelet Transform

Abstract: Application of an empirical wavelet transform (EWT)-based time-frequency technique is proposed in this paper for the estimation of power-quality indices (PQIs). This technique first estimates the frequency components present in the distorted signal, computes the boundaries, and then filtering is done based on the boundaries computed. This technique is effective and offers many advantages over other techniques because the scaling function and wavelets adapt themselves according to the information contained in the signal and no prior information regarding the signal is required. Besides this, the advantages of the discrete wavelet transform (DWT), wavelet packet transform (WPT), short-time Fourier transform (STFT), windowing techniques, and filter bank techniques also hold. Simulated and experimental test signals with power-quality disturbances have been analyzed and the effectiveness of this technique has been shown by comparing the PQIs estimated by using the proposed method with those obtained using the DWT and WPT. The results confirm that the EWT efficiently extracts the mono component signals from the actual distorted signal and thereby accurately estimates the PQIs.

Investigations on the influence of composition in the development of Ni-Ti shape memory alloy using laser based additive manufacturing

Abstract: Among the various shaped memory alloys (SMA), nitinol (Ni–Ti alloy) finds applications in automotive, aerospace, biomedical and robotics The conventional route of fabrication of SMA has several limitations, like formation of stable secondary phases, fabrication of simple geometries, etc This paper reports a novel method of fabricating SMA using a laser based additive manufacturing technique Three different compositions of Ni and Ti powders (Ni-45% Ti-55%; Ni-50% Ti-50%; Ni-55% Ti45%) were pre-mixed using ball-milling and laser based additive manufacturing system was employed to fabricate circular rings The material properties of fabricated rings were evaluated using Scanning Electron Microscopy (SEM), Differential scanning calorimeter (DSC), X-ray diffraction (XRD) system and micro-hardness test All the characterized results showed that SMA could be manufactured using the laser based additive manufacturing process The properties of laser additive manufactured SMA (Ni-50% Ti-50%) were found to be close to that of conventionally processed SMA

A possible explanation of low energy γ-ray excess from galactic centre and Fermi bubble by a Dark Matter model with two real scalars

Abstract: We promote the idea of multi-component Dark Matter (DM) to explain results from both direct and indirect detection experiments. In these models as contribution of each DM candidate to relic abundance is summed up to meet WMAP/Planck measurements of Ω{sub DM}, these candidates have larger annihilation cross-sections compared to the single-component DM models. We illustrate this fact by introducing an extra scalar to the popular single real scalar DM model. We also present detailed calculations for the vacuum stability bounds, perturbative unitarity and triviality constraints on this model. As direct detection experimental results still show some conflict, we kept our options open, discussing different scenarios with different DM mass zones. In the framework of our model we make an interesting observation: the existing direct detection experiments like CDMS II, CoGeNT, CRESST II, XENON 100 or LUX together with the observation of excess low energy γ-ray from galactic centre and Fermi bubble by Fermi Gamma-ray Space Telescope (FGST) already have the capability to distinguish between different DM halo profiles.

Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition

Abstract: In this paper, a variational mode decomposition (VMD) based method has been proposed for the instantaneous detection of voiced/non-voiced (V/NV) regions in the speech signals. In the proposed method, the VMD is applied in iterative way with specific input parameters. Firstly, the VMD decomposes the speech signal into two components, then, the VMD is applied successively on one of these two components based on suitably defined convergence criteria. It has been shown that the VMD applied in iterative way behaves as a low-pass filter and after convergence it provides separation of the fundamental frequency (F0) component from the speech signal. The envelope of the F0 component of the speech signal has been obtained using an analytical model based on single degree of freedom (SDOF). Automatic threshold has been computed from the obtained envelope in order to detect the V/NV regions in speech signals. The proposed method has been studied on speech signals and the corresponding electroglottograph (EGG) signals from the CMU-Arctic database in different noise conditions obtained from the NOISEX-92 database. Experimental results at various signal to noise ratios (SNRs) are included in order to show the effectiveness of the proposed method compared to the other existing methods for V/NV detection in speech signals.

Aggregation induced emission and mechanochromism in pyrenoimidazoles

Abstract: Pyrene-based solid state emitters 3a and 3b were designed and synthesized by the Pd-catalyzed Suzuki cross-coupling reaction of bromopyrenoimidazole 2 with 4-(1,2,2-triphenylvinyl)phenylboronic acid pinacol ester and 2-(4-pinacolatoboronphenyl)-3,3-diphenylacrylonitrile. The single crystal X-ray structure of 3a was reported and revealed the twisted conformation. Their photophysical, aggregation induced emission (AIE) and mechanochromic properties were studied. Pyrenoimidazoles 3a and 3b exhibit strong AIE. 3b shows different colored emission with varying water fraction. 3a and 3b exhibit reversible mechanochromic behavior with color contrast between blue and green. The enhanced conjugation and increased amorphous nature observed after grinding are associated with mechanochromism in pyrenoimidazoles 3a and 3b.

Different domestic designs of solar stills: A review

Abstract: Access to safe, fresh and clean drinking water is one of the major problems in different parts of the world. Among many water purification technologies solar desalination/distillation/purification is one of the most sustainable and attractive method employed to meet the supply of clean drinkable water in remote areas at a very reasonable cost. Over the past three decades, there have been numerous designs of solar still system developed worldwide. However the technology is not commercialized and standardized because of its lower yield. This article provides a comprehensive review of the various designs of solar stills used at domestic level. Performance parameters like heat transfer analysis, energy analysis, exergy analysis, thermal efficiency and economic analysis have been presented for the domestic designs of solar stills. Though solar still have not been successfully commercialized as yet, with the ongoing research efforts, they can be modified and improved for future domestic applications.

Automatic diagnosis of septal defects based on tunable-Q wavelet transform of cardiac sound signals

Abstract: We propose a new method for diagnosis of septal defects using TQWT.New feature set based on SAMDF derived from TQWT has been proposed.The effects of Q and decomposition levels on classification performance have been evaluated.Results have been evaluated with classification performance evaluation parameters and ROC graphs.Performance has been compared with existing TQWT based method with same datasets. Accurate and quick diagnosis of cardiac disorders like septal defects can be performed by automatic analysis of cardiac sound signals using advanced signal processing methods. In this paper, we present a new method for diagnosis of septal defects from cardiac sound signals using tunable-Q wavelet transform (TQWT) based features. To start with, the established constrained TQWT based approach has been used to derive the heart beat cycles from cardiac sound signals. Then the TQWT based decomposition of segmented heart beat cycles has been performed up to a certain level. The combinations of sub-bands obtained from TQWT based decomposition can be used to extract the diagnostic features. The correlation between sub-bands can characterize the various types of murmurs in cardiac sound signals. Therefore, in order to represent the murmurs in cardiac sound signals, proposed feature set was obtained with sum of average magnitude difference function (SAMDF) that have been computed from reconstruction of decomposed sub-bands. In search of effective feature set based on SAMDF that could provide significant classification performance, various decomposition levels have been examined. Moreover, in order to establish the usefulness of the proposed method for diagnosis of septal defects, besides cardiac sound signals for septal defects and normal, this study covers signals to be detected for valvular defects and other defects like ventricular hypertrophy, constrictive pericarditis etc. as available from publicly available datasets. The classification has been performed using least squares support vector machine (LS-SVM) with different kernel functions. At each decomposition level under study, the effect of quality-factor ( Q ) of the TQWT from 1 to 50 on classification performance has been evaluated. Moreover, in order to show the effectiveness of the proposed method, results have been compared with existing method.

An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface

Abstract: In this paper, we present a new filtering method based on the empirical mode decomposition (EMD) for classification of motor imagery (MI) electroencephalogram (EEG) signals for enhancing brain-computer interface (BCI). The EMD method decomposes EEG signals into a set of intrinsic mode functions (IMFs). These IMFs can be considered narrow-band, amplitude and frequency modulated (AM-FM) signals. The mean frequency measure of these IMFs has been used to combine these IMFs in order to obtain the enhanced EEG signals which have major contributions due to μ and β rhythms. The main aim of the proposed method is to filter EEG signals before feature extraction and classification to enhance the features separability and ultimately the BCI task classification performance. The features namely, Hjorth and band power features computed from the enhanced EEG signals, have been used as a feature set for classification of left hand and right hand MIs using a linear discriminant analysis (LDA) based classification method. Significant superior performance is obtained when the method is tested on the BCI competition IV datasets, which demonstrates the effectiveness of the proposed method.

Security Architecture for Cloud Computing

Abstract: Clouds need to address three security issues: confidentiality, integrity, and availability. Security architecture for cloud computing is designed based on the functional architecture. The approach is to enhance the components of a functional architecture with additional components providing various security services. This is an extension of SaaS concept to have several security components that are common to all application and services. Various cloud security issues are discussed in this chapter.

Unsymmetrical Donor-Acceptor-Acceptor-π-Donor Type Benzothiadiazole-Based Small Molecule for a Solution Processed Bulk Heterojunction Organic Solar Cell.

Abstract: A D1–A–A′−π–D2 type (D = donor; A = acceptor) unsymmetrical small molecule denoted as BTD3 containing different end group donor moieties has been designed and synthesized for use as a donor in the solution processable bulk heterojunction (BHJ) solar cell. The BTD3 exhibits a low HOMO–LUMO gap of 1.68 eV and deeper HOMO energy level (−5.5 eV). Its LUMO energy level (−3.65 eV) is compatible with the LUMO level of PC71BM to facilitate the electron transfer from BTD3 to PC71BM in the BHJ solar cell. The solution processed BHJ solar cell with optimized BTD3:PC71BM active layer processed with THF solvent exhibited a PCE of 3.15% with Jsc = 7.45 mA/cm2, Voc = 0.94 V, and FF = 0.45. Moreover, the device with optimized concentration of 3 vol. % 1-chloronaphthalene (CN) additive, i.e., CN/THF, showed significant enhancement in PCE up to 4.61% (Jsc = 9.48 mA/cm2, Voc = 0.90 V, and FF = 0.54). The improvement in the PCE has been attributed to the appropriate nanoscale phase separation morphology, balance charge trans...

Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals

Abstract: We propose new features for analysis of normal and diabetic RR-interval signals.Features are extracted from intrinsic mode functions of RR-interval signals.Two unique visual plots are proposed for diagnosis of diabetes.Proposed features are suitable for discrimination of normal and diabetic classes. Large number of people are affected by Diabetes Mellitus (DM) which is difficult to cure due to its chronic nature and genetic link. The uncontrolled diabetes may lead to heart related problems. Therefore, the diagnosis and monitoring of diabetes is of great importance. The automatic detection of diabetes can be performed using RR-interval signals. The RR-interval signals are nonlinear and non-stationary in nature. Hence linear methods may not be able to capture the hidden information present in the signal. In this paper, a new nonlinear method based on empirical mode decomposition (EMD) is proposed to discriminate between diabetic and normal RR-interval signals. The mean frequency parameter using Fourier-Bessel series expansion ( MF FB ) and the two bandwidth parameters namely, amplitude modulation bandwidth ( B AM ) and frequency modulation bandwidth ( B FM ) extracted from the intrinsic mode functions (IMFs) obtained from the EMD of RR-interval signals are used to discriminate the two groups. Unique representations such as analytic signal representation (ASR) and second order difference plot (SODP) for IMFs of RR-interval signals are also proposed to differentiate the two groups. The area parameters are computed from ASR and SODP of IMFs of RR-interval signals. Area computed from these representation as area corresponding to the 95% central tendency measure (CTM) of ASR of IMFs ( A ASR ) and 95% confidence ellipse area of SODP of IMF ( A SODP ) are also proposed to discriminate diabetic and normal RR-interval signals. Overall, five features are extracted from IMFs of RR-interval signals namely MF FB , B AM , B FM , A ASR and A SODP . Kruskal-Wallis statistical test is used to measure the discrimination ability of the proposed features for detection of diabetic RR-interval signals. Results obtained from proposed methodology indicate that these features provide the statistically significant difference between diabetic and normal classes.

Measurement of electrons from semileptonic heavy-flavor hadron decays in pp collisions at s =2.76TeV

Abstract: The pT-differential production cross section of electrons from semileptonic decays of heavy-flavor hadrons has been measured at midrapidity in proton-proton collisions at s=2.76TeV in the transverse momentum range 0.5

Nickel(II) complexes with a flexible piperazinyl moiety: studies on DNA and protein binding and catecholase like properties

Abstract: Four new mononuclear Ni(II) complexes [Ni(L1)]ClO4 (1a), [Ni(L2)]ClO4(1b), [Ni(SCN)3(CH3OH)(aminoethylpiperazineH)] (2a), and [Ni(DMSO)4(aminoethylpiperazineH)](ClO4)3(2b)have been synthesized from two Schiff base ligands [L1 = 1-phenyl-3-((2-(piperidin-4-yl)ethyl)imino)but-1-en-1-ol and L2 = 4-((2-(piperazin-1-yl)ethyl)imino)pent-2-en-2-ol] by exploiting the flexibility of the piperazinyl moiety. Structural analysis reveals that 1a and 1b are square planar complexes with piperazine rings in boat conformations whereas hydrolysis of Schiff bases (L1 and L2) occurs during formation of octahedral complexes (2a and 2b) with piperazine rings in chair conformations. Screening tests were conducted to quantify the binding ability of complexes (1a, 1b and 2a) towards DNA, BSA and HSA and it was found that square planar complexes (1a and 1b) showed more effective binding properties over octahedral complex (2a). Furthermore, enzyme kinetic studies reflect that square planar complexes (1a and 1b) are also effective in mimicking catecholase like activities over octahedral complex (2a). Among all the complexes, 1a was found to be the most promising molecule among the series due to its large binding affinity towards different bio-macromolecules and higher T.O.N in the catechol oxidation reaction.

Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals

Abstract: A Raman optical fiber distributed temperature sensor, using a wavelet transform based signal processing technique for backscattered anti-Stokes and Stokes signals, is presented. This technique mainly performs two functions. First, it equalizes the wavelength dependent optical fiber attenuation of two Raman backscattered signals and second, denoises these Raman signals without generating any appreciable spatial inaccuracy in locating the hot zones. The proposed technique enables automatic measurement of distributed temperature profile that has better temperature accuracy and very small spatial error in detecting the location of hot zones. The accuracy achieved in temperature measurement with processed Raman signals is much better than the accuracy obtained with unprocessed signals. Results show a maximum temperature error of ±3.5 °C in a temperature range of 25–295 °C and a maximum spatial error of ±3 cm in locating the hot zones over a sensing length of 205 m with spatial resolution of 1 m. The proposed technique has been used for the development of a prototype Raman optical fiber distributed temperature sensor (ROFDTS) system which employs 200/220 μm Polyimide coated Multimode optical fiber. The technique is much simpler compared to other complex techniques described in the literature and is suitable for general temperature sensing applications. The technique supports automatic and dynamic self-calibration of ROFDTS to take care of slow variations/drifts in the observed Raman signals which are due to fluctuations in laser power and laser–fiber coupling. A self-calibration setup has also been developed to track the changes in the temperature of calibration zone.